WE‐C‐J‐6C‐09: Cone Beam Digital Tomosynthesis (CBDT): An Alternative to Cone Beam Computed Tomography (CBCT) for Image‐Guided Radiation Therapy
Bibliographic record
Abstract
Purpose: To investigate the feasibility of using a new imaging technique, i.e., Cone Beam Digital Tomosynthesis (CBDT) as an alternative to the Cone Beam Computed Tomography (CBCT) approach for creating 3D cross sectional images of a patient in the radiotherapy treatment room. Method and Materials: Similar to the CBCT approach, the CBDT uses an x‐ray source (either a kV source or a MV source on a Linac or another radiation‐emitting device) and an x‐ray detector to acquire projection data by rotating them around a patient simultaneously. Unlike CBCT, CBDT utilizes partial scans, typically in the range of 20–60 degrees of gantry arc. The main advantage of the CBDT approach is that it takes less time to perform image acquisition and reconstruction. An experimental CBDT system has been built on a Linac with a recently developed flat‐panel detector. A novel filtered backprojection algorithm was developed for CBDT reconstructions. CBDT phantom images have been generated for different degrees of gantry arc and beamline configurations and compared to those from CBCT. Results: Cross‐sectional images of a Rando phantom were generated with comparable image quality to CBCT using as small as ∼ 22 degree gantry arc. The planes of CBDT reconstruction are orthogonal to the x‐ray beam at the midpoint of the arc. These reconstructed planes are of particular relevance to image‐guided radiation therapy, because they depict anatomy along planes that are most relevant to the treatment beam, i.e., orthogonal to the beam axis. Conclusion: We have demonstrated the feasibility of using CBDT to acquire cross‐sectional images of an object in the treatment room to guide radiotherapy treatment. Compared to CBCT, the CBDT approach is much faster with acceptable image quality in the planes most relevant to the treatment. Conflict of Interest: This work was supported by Siemens Medical Solutions USA, Inc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".